We propose a new performance predictor \(f^{\prime }\)  called EmProx Score (Embedding Proximity Score) as a faster alternative for Neural Architecture Optimization (NAO) [1]}. Similar to NAO, EmProx uses an encoder-decoder framework to map candidate architectures to a continuous embedding space. However, instead of training an multi-layer perceptron (MLP) on the embedding vectors to predict the accuracy of a candidate, EmProx uses weighted k-nearest neighbors on the embedding vectors. As such, EmProx estimates the performance of the query architecture based on the distance and accuracy of its nearest neighbors in the embedding space, which can be computed efficiently.
